Recently, deep learning methods have been shown to produce promising results for multiband fusion [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}. These methods use a deep neural network to learn the end-to-end functional relationship between the observed and ground truth images.
Although these methods have excellent performance, sufficient ground-truth is required for training a deep neural network, and this is difficult for remote sensing applications. Limited data ultimately affects the generalization capacity of the trained network [15]}, [16]}.
Last, but not the least, the network is trained to work with a fixed forward model and cannot directly be used for other models. It is thus not surprising that state-of-the-art techniques for multiband fusion are based on variational models [16]}.
